Michigan State University, Department of Electrical and Computer Engineering, East Lansing, MI 48824, USA.
J Neurosci Methods. 2022 Jul 1;376:109610. doi: 10.1016/j.jneumeth.2022.109610. Epub 2022 Apr 30.
Neuronal transmission and communication are enabled by the interactions across multiple oscillatory frequencies. Phase amplitude coupling (PAC) quantifies these interactions during cognitive brain functions. PAC is defined as the modulation of the amplitude of the high frequency rhythm by the phase of the low frequency rhythm. Existing PAC measures are limited to quantifying the average coupling within a time window of interest. However, as PAC is dynamic, it is necessary to quantify time-varying PAC. Existing time-varying PAC approaches are based on using a sliding window approach. These approaches do not adapt to the signal dynamics, and thus the arbitrary selection of the window length substantially hampers PAC estimation.
To address the limitations of sliding window PAC estimation approaches, in this paper, we introduce a dynamic PAC measure that relies on matching pursuit (MP). This approach decomposes the signal into time and frequency localized atoms that best describe the signal. Dynamic PAC is quantified by computing the coupling between these time and frequency localized atoms. As such, the proposed approach is data-driven and tracks the change of PAC with time. We evaluate the proposed method on both synthesized and real electroencephalogram (EEG) data.
The results from synthesized data show that the proposed method detects the coupled frequencies and the time variation of the coupling correctly with high time and frequency resolution. The analysis of EEG data revealed theta-gamma and alpha-gamma PAC during response and post-response time intervals.
COMPARISON WITH EXISTING METHOD(S): Compared to the existing sliding window based approach, the proposed MP based dynamic PAC measure is more effective at capturing PAC within a short time window and is more robust to noise. This is because this method quantifies the low frequency phase and high frequency amplitude components from the time and frequency localized MP atoms and, as such, can capture the signal dynamics.
We posit that the proposed MP based data-driven approach offers a more robust and possibly more sensitive method to effectively quantify and track dynamic PAC.
神经元的传递和通讯是通过多个振荡频率的相互作用实现的。相位振幅耦合(PAC)量化了认知脑功能期间这些相互作用。PAC 被定义为高频节律的幅度被低频节律的相位调制。现有的 PAC 测量方法仅限于量化感兴趣时间窗口内的平均耦合。然而,由于 PAC 是动态的,因此有必要量化时变 PAC。现有的时变 PAC 方法基于使用滑动窗口方法。这些方法不能适应信号动态,因此任意选择窗口长度会严重妨碍 PAC 估计。
为了解决滑动窗口 PAC 估计方法的局限性,在本文中,我们引入了一种依赖匹配追踪(MP)的动态 PAC 度量方法。该方法将信号分解为时间和频率局部原子,这些原子最好地描述了信号。通过计算这些时间和频率局部原子之间的耦合来量化动态 PAC。因此,所提出的方法是数据驱动的,并随时间跟踪 PAC 的变化。我们在合成和真实脑电图(EEG)数据上评估了所提出的方法。
合成数据的结果表明,该方法以高时间和频率分辨率正确检测到耦合频率和耦合的时间变化。对 EEG 数据的分析揭示了在响应和响应后时间间隔期间存在 theta-gamma 和 alpha-gamma PAC。
与现有的基于滑动窗口的方法相比,所提出的基于 MP 的动态 PAC 度量方法在短时间窗口内更有效地捕获 PAC,并且对噪声更鲁棒。这是因为该方法从时间和频率局部化的 MP 原子量化低频相位和高频幅度分量,因此可以捕获信号动态。
我们假设,所提出的基于 MP 的数据驱动方法提供了一种更稳健且可能更敏感的方法,可以有效地量化和跟踪动态 PAC。